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add stuff why inbalanced doesn't work for caml
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lukas-heilgenbrunner 2025-01-14 19:39:41 +01:00
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@ -151,7 +151,7 @@ In a real world scenario this should not be the case because the support set is
=== Results
The results of P>M>F look very promising and improve by a large margin over the ResNet50 method.
In @pmfbottleperfa the model reached an accuracy of 79% with 5 shots / 4 way classification.
The 2 way classification (faulty or not) performed even better and peaked at 94% accuracy with 5 shots.#todo[Add somehow that all classes are stacked]
The 2 way classification (faulty or not) performed even better and peaked at 94% accuracy with 5 shots.
Similar to the ResNet50 method in @resnet50perf the tests with an inbalanced class distribution performed worse than with balanced classes.
So it is clearly a bad idea to add more good shots to the support set.
@ -183,6 +183,9 @@ So it is clearly a bad idea to add more good shots to the support set.
== CAML
=== Approach
For the CAML implementation the pretrained model weights from the original paper were used.
This brings the limitation of a maximum squence length to the non-causal sequence model.
This is the reason why for this method the two imbalanced test cases couldn't be conducted.
As a feture extractor a ViT-B/16 model was used, which is a Vision Transformer with a patch size of 16.
This feature extractor was already pretrained when used by the authors of the original paper.
For the non-causal sequence model a transformer model was used